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 Duration 21 hours

Course Outline

Foundations of Hybrid AI Deployment

  • Exploring hybrid, cloud, and edge deployment models
  • Characteristics of AI workloads and associated infrastructure constraints
  • Selecting optimal deployment topologies

Containerizing AI Workloads with Docker

  • Creating GPU and CPU-based inference containers
  • Managing secure images and registry configurations
  • Establishing reproducible environments for AI applications

Deploying AI Services in Cloud Environments

  • Executing inference on AWS, Azure, and GCP using Docker
  • Provisioning cloud compute resources for model serving
  • Securing cloud-hosted AI endpoints

Edge and On-Premise Deployment Strategies

  • Running AI on IoT devices, gateways, and microservers
  • Utilizing lightweight runtimes suited for edge environments
  • Handling intermittent connectivity and local data persistence

Hybrid Networking and Secure Connectivity

  • Establishing secure tunnels between edge and cloud nodes
  • Implementing certificates, secrets, and token-based access controls
  • Tuning performance for low-latency inference

Orchestrating Distributed AI Deployments

  • Employing K3s, K8s, or lightweight orchestration for hybrid architectures
  • Managing service discovery and workload scheduling
  • Automating rollout strategies across multiple locations

Monitoring and Observability Across Environments

  • Tracking inference performance across distributed sites
  • Implementing centralized logging for hybrid AI systems
  • Detecting failures and triggering automated recovery

Scaling and Optimizing Hybrid AI Systems

  • Scaling edge clusters and cloud nodes
  • Optimizing bandwidth usage and caching mechanisms
  • Balancing compute loads between cloud and edge resources

Summary and Next Steps

Requirements

  • A solid understanding of containerization principles
  • Proficiency with Linux command-line operations
  • Familiarity with AI model deployment workflows

Target Audience

  • Infrastructure architects
  • Site Reliability Engineers (SREs)
  • Edge and IoT developers

Number of participants


Price per participant

Open Training Courses require 5+ participants.

Testimonials (2)

As i said before , for a person like me (no exp. ) this was a gateway to understanding features and functions with these programs/tools & etc. .

Patrick V. Duylovski - UBB + DZI (KBC GROUP)
Course - Docker and Kubernetes

multi-tiered, structured course programme.

Bert Paelinckx - Cube SoftwareSolutions
Course - Introduction to Docker

Upcoming Courses

Hybrid AI Deployment: Docker, Cloud, and Edge Integration

2026-11-30 09:30
21 hours
Copenhagen, City Center
5208 EUR (Online)
5808 EUR (Classroom)

Hybrid AI Deployment: Docker, Cloud, and Edge Integration

2026-12-14 09:30
21 hours
København, Copenhagen
5208 EUR (Online)
5808 EUR (Classroom)

Hybrid AI Deployment: Docker, Cloud, and Edge Integration

2026-12-28 09:30
21 hours
Copenhagen, City Center
5208 EUR (Online)
5808 EUR (Classroom)

Hybrid AI Deployment: Docker, Cloud, and Edge Integration

2027-01-11 09:30
21 hours
København, Copenhagen
5208 EUR (Online)
5808 EUR (Classroom)

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